دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 14 3 2026 09 23 The Impact of Bank Capital on Liquidity Creation Across Quantiles: A Comparative Study of Developed and Developing Countries Using Quantile Regression Approach تأثیر سرمایه بانک بر خلق نقدینگی در دهک‌های مختلف: مطالعه تطبیقی کشورهای توسعه‌یافته و درحال‌توسعه با رویکرد رگرسیون کوانتایل 1 30 29704 10.22108/amf.2025.144564.1964 FA محمد سالم ماضی دانشجوی دکتری، گروه اقتصاد، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران علی رضازاده دانشیار، گروه اقتصاد، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران شهاب جهانگیری دانشیار، گروه اقتصاد، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران رامین بشیر خداپرستی دانشیار، گروه امور مالی و بیمه، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران Journal Article 2025 03 05 This study investigates the nonlinear relationship between bank capital and liquidity creation across the distribution of liquidity creation (by deciles) in both developed and developing countries. Recognizing the critical role of liquidity creation in fostering financial stability and economic growth, the analysis addresses how the magnitude and direction of bank capital’s impact may vary across different levels of liquidity creation and country types. Employing quantile regression on a sample of 59 developing and 37 developed countries from 2004 to 2023, the findings reveal a consistently negative and significant effect of bank capital on liquidity creation throughout all deciles. However, this negative effect attenuates in higher deciles for developed countries, whereas it intensifies in developing countries. Furthermore, economic growth, financial inclusion, and financial development indices generally exhibit positive and significant effects. Conversely, the financial stability index demonstrates a significant negative impact in the lower deciles for developed nations and the higher deciles for developing economies. These contrasting outcomes underscore fundamental differences in liquidity creation mechanisms across countries and emphasize the necessity of a disaggregated, context-specific approach to banking regulation and policy formulation. Keywords: Liquidity Creation, Bank Capital, Quantile Regression, Developing and Developed Countries JEL Classification: G28, G21, C23 Introduction Liquidity creation, a fundamental function of banks within the modern financial system, involves the transformation of short-term liabilities into long-term loans, a process inherently coupled with risk intermediation. Within this framework, bank capital serves a dual and potentially contradictory role: while it provides a crucial buffer against losses that can enhance a bank's capacity to assume risk and create liquidity, it may also constrain the resources available for lending. This tension suggests a nonlinear relationship, wherein the effect of capital on liquidity creation may vary across its distribution. At lower capital levels, increases might initially curb liquidity creation by reducing lendable funds, whereas at higher levels, the enhanced risk-absorbing capacity could facilitate greater liquidity creation. This non-uniformity implies that the impact of capital regulations is likely heterogeneous across institutions, challenging the efficacy of a one-size-fits-all regulatory approach. Motivated by this complexity, the present study employs a quantile regression (QR) methodology to empirically investigate the nuanced relationship between bank capital and liquidity creation across different deciles of the liquidity creation distribution. By analyzing a global sample of 37 developed and 59 developing countries over the period 2004–2023 within separate models, this research aims to provide a more disaggregated understanding critical for designing targeted prudential policies. Materials & Methods This study employs a panel quantile regression (QR) methodology to examine the nuanced impact of bank capital on liquidity creation across the entire conditional distribution of the latter. Selected for its capacity to provide a comprehensive analysis beyond the conditional mean, this approach is particularly advantageous for capturing potential heterogeneity in the relationship across different deciles, including the tails of the distribution. In contrast to conventional ordinary least squares (OLS) regression, QR estimates coefficients by minimizing a weighted sum of absolute deviations, known as the Least Absolute Deviation (LAD) method. This technique is robust to non-normal error distributions, heteroscedasticity, and the presence of outliers, thereby yielding more reliable and efficient estimates for our financial dataset, which may exhibit such characteristics. Aligned with this rationale, we estimate a nonlinear panel quantile regression model for a global sample comprising 37 developed and 59 developing countries over the period 2004–2023. The general empirical specification, adapted from the frameworks established by MazioudChaabouni et al. (2018) and Gupta et al. (2023), is formally defined as follows: (1) in which, LM represents liquidity creation, LCAR represents bank regulatory capital (transition variable), LBZSCORE is the financial stability, LGDPP is the variable of economic growth, LATM represents the financial inclusion index, and LFSD is the financial development index. Findings The stationarity of the variables was assessed using the Levin, Lin, and Chu (LLC) unit root test. As detailed in Table 1, the results confirm that all variables are stationary at the 5% significance level, incorporating an intercept term. Subsequently, the core empirical analysis, illustrated in Figure 5-a, reveals a statistically significant negative relationship between bank capital (LCAR) and liquidity creation (LM) across all deciles for developed countries. Notably, the magnitude of this negative effect exhibits a diminishing pattern, weakening progressively throughout the higher deciles of the liquidity creation distribution. Chart (5-a). Trends in variables in deciles in developed countries Furthermore, the results for developing countries, as visualized in Figure 5-b, indicates a distinct and evolving relationship. The effect of bank capital (LCAR) on liquidity creation (LM) is statistically insignificant and positive in the first decile. However, this relationship transitions to a negative and statistically significant influence beginning in the second decile. Moreover, the magnitude of this adverse effect demonstrates a pronounced intensification across the higher deciles of the distribution. Chart (5-b). Trends in variables in deciles in developing countries Conclusion and discussion In conclusion, this study establishes that the relationship between bank capital and liquidity creation is not only nonlinear but also contingent upon a country's developmental context and its position within the liquidity creation distribution. The analysis reveals a consistently negative yet diminishing effect across deciles for developed nations, while in developing countries, the relationship manifests as negative and significant from the second decile onward, intensifying markedly at higher levels. This stark heterogeneity underscores fundamental differences in the operational mechanisms of financial intermediation and the distinct role capital plays across diverse economic landscapes. Furthermore, control variables corroborate this complexity; economic growth, financial inclusion, and financial development predominantly exert a positive influence on liquidity creation, whereas financial stability exhibits a significant negative impact in specific deciles, particularly within developing economies. Collectively, these findings carry substantial policy implications, strongly advocating for a disaggregated regulatory approach. Policymakers must therefore eschew uniform, one-size-fits-all capital regulations in favor of frameworks meticulously tailored to a country's level of financial development and the specific characteristics of its banking institutions. The application of quantile regression in this analysis proves indispensable, providing the granular insights necessary for such precise and effective policy formulation. مسئلۀ اصلی این پژوهش، بررسی رابطۀ غیرخطی میان سرمایۀ بانکی و خلق نقدینگی در سطوح مختلف توزیع خلق نقدینگی (دهک‌ها) در کشورهای توسعه‌یافته و درحال‌توسعه است. اهمیت این موضوع از آنجا ناشی می‌شود که خلق نقدینگی نقش کلیدی در ثبات مالی و رشد اقتصادی ایفا می‌کند؛ اما میزان و جهت اثرگذاری سرمایۀ بانکی ممکن است در سطوح مختلف خلق نقدینگی و بین کشورها متفاوت باشد؛ در این راستا، مطالعۀ حاضر با استفاده از رگرسیون کوانتایل، اثر سرمایۀ بانکی را بر خلق نقدینگی در دو مدل جداگانه برای 59 کشور درحال‌توسعه و 37 کشور توسعه‌یافته طی دورۀ 2004 تا 2023 بررسی می‌کند. یافته‌ها نشان می‌دهد که سرمایۀ بانکی در تمامی دهک‌ها اثری منفی و معنادار بر خلق نقدینگی دارد، به‌گونه‌ای که این اثر در دهک‌های بالای کشورهای توسعه‌یافته کاهش یافته، ولی در کشورهای درحال‌توسعه افزایش یافته است. همچنین، رشد اقتصادی، شاخص شمول مالی و توسعۀ مالی عمدتاً اثر مثبت و معنادار دارند، درحالی‌که شاخص ثبات مالی در دهک‌های پایین کشورهای توسعه‌یافته و دهک‌های بالای کشورهای درحال‌توسعه اثر منفی معناداری دارد. این نتایج گویای تفاوت در سازوکار خلق نقدینگی بین کشورها و اهمیت رویکرد تفکیکی در سیاست‌گذاری است.

https://amf.ui.ac.ir/article_29704_a231a48bcff15900e8f9ceac00b84196.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 14 3 2026 09 23 Designing an Early Warning System to Predict Price Bubbles in the Tehran Stock Exchange Using Deep Learning طراحی سیستم هشدار سریع جهت پیش‎‌بینی حباب‎‌های بورس اوراق بهادار تهران با رویکرد یادگیری عمیق 31 58 29846 10.22108/amf.2025.143670.1943 FA مرتضی مزارعی دانشجوی دکتری گروه مدیریت مالی، پردیس بین‌المللی کیش، دانشگاه تهران، کیش، ایران 0009-0008-0601-7432 عزت اله عباسیان استاد، گروه مهندسی مالی، دانشکدگان مدیریت، دانشگاه تهران، تهران، ایران ابراهیم نصیرالاسلامی استادیار، گروه آمار، دانشکده علوم، دانشگاه بوعلی‌سینا، همدان، ایران Journal Article 2024 12 28 The primary objective of this study is to design an Early Warning System (EWS) based on a Long Short-Term Memory (LSTM) architecture for the timely forecasting of price bubbles in the Tehran Stock Exchange (TSE). A secondary objective is to compare the predictive performance of this model against a Logistic Regression (LR) benchmark, using evaluation metrics such as the AUC-ROC and confusion matrix. The system's performance was evaluated on five selected TSE indices. Utilizing monthly data from 2002 to 2023, bubble periods were identified via the Generalized Supremum Augmented Dickey-Fuller (GSADF) test and represented as a binary variable. This bubble variable was then modeled using price changes of key warning indicators. The proposed deep learning-based system achieved predictive accuracy ranging from 73% to 81%, with the highest performance on the Total Index (81%) and the lowest on the Basic Metals Index (73%). A comparative analysis demonstrates that the LSTM model outperformed the LR model across all selected indices. The evaluation metrics confirm the superior performance of the LSTM model. To the best of our knowledge, this study presents the first reported EWS designed for predicting price bubbles in this context using a deep learning approach. Keywords: Generalized Supremum Augmented Dickey-Fuller Test (GSADF), Early Warning System (EWS), Logistic Regression (LR), Long Short-Term Memory (LSTM) JEL Classification: G01, G32, C45, C53 Introduction The bursting of price bubbles can precipitate financial crises, rendering the study of bubbles and market collapses highly consequential for investment portfolio risk management (Kaliva & Koskinen, 2008). The early identification of such bubbles and the forecasting of their trajectories are therefore crucial for policymakers and market participants, as it enables preventive measures to mitigate or avert financial turmoil. Consequently, the development and deployment of financial EWS are essential to actively reduce economic vulnerabilities and counteract price bubbles (Claessens & Kose, 2013). As Phillips et al. (2015) note, an effective EWS must achieve a high degree of accurate detection to facilitate swift and effective policy implementation, while simultaneously maintaining a low false-positive rate to avoid unnecessary policy actions. Analyzing historical bubble episodes and the factors influencing their emergence is thus fundamental to informed decision-making and the control of market irregularities (Sadeghisharif et al., 2017). In this context, deep learning algorithms have significantly advanced machine-learning models by offering greater computational speed and predictive precision. The rapid evolution of this field has attracted considerable attention from economists addressing a range of problems, particularly in the domain of asset price forecasting (Khaliliaraghi et al., 2022). The primary objective of this study is to evaluate bubble periods in selected Tehran Stock Exchange (TSE) indices and, by incorporating price changes from a set of early warning indicators, to design a bubble prediction system using an LSTM model. This research further aims to compare the predictive accuracy and quality of the LSTM model against an LR benchmark. Accordingly, this research seeks to answer the following question: Does the employment of a deep learning approach improve the accuracy and quality of bubble forecasts across all selected indices in this study? Materials and Methods The target variables in this study were identified through a comparative analysis. Five TSE indices were selected: the Total Index, the 50 Most Active Companies Index, the Industry Index, the Financial Index, and the Basic Metals Index. Monthly data for these indices were collected for the period from 2002 to 2023. Bubble episodes were identified using the GSADF test, and the resulting bubble signals were used to construct a dummy bubble variable, which served as the dependent variable in the subsequent modeling. Based on a literature review, eighteen influential variables were drawn from macroeconomic indicators, market information, valuation multiples, and commodity prices to serve as independent warning indicators. The returns for these variables were calculated as logarithmic changes. Given the time-series nature of the data, these indicators were structured with a 5-period lag and normalized to serve as input features for the models. The contribution of these variables to predictive power, while indirect, is evidenced through the final model performance. Subsequently, bubble-forecasting models were developed and an EWS was designed using a Recurrent Neural Network with an LSTM architecture as the deep learning approach, alongside an LR model as a classical machine-learning benchmark. Finally, model performance was evaluated based on the model type and stock index, using forecasting accuracy, the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and the confusion matrix. This evaluation framework allowed for a comprehensive assessment and comparison of the models' predictive capabilities. Findings As illustrated in Table 1, the highest forecasting accuracy for the LR model was achieved by the Financial Index at 79%, while the lowest was observed for the Basic Metals Index at 65%. The AUC evaluation for the LR model indicates its strongest performance on the Financial Index (92%) and its weakest on the Basic Metals Index (79%). Furthermore, the confusion matrix assessment for the LR model reveals notably low true positive rates for the test data, ranging from 0% to 17%. According to the results in Table 2, the LSTM model attained its highest forecasting accuracy on the Total Index (81%) and its lowest on the Basic Metals Index (73%). A comparative analysis of predictive accuracy demonstrates that the LSTM model outperformed the LR benchmark across all indices. The AUC evaluation for the LSTM model also shows its best performance on the Financial Index (92%) and its lowest on the Basic Metals Index (81%). The confusion matrix for the LSTM model indicates substantially higher true positive rates, ranging from 38% to 71%, with the highest rate for the Total Index (71%) and the lowest for the 50 Most Active Companies Index (38%). Table (1) Evaluation of the accuracy of the LR model for training and testing datasets by index Testing Training Indices 0.69 0.83 Total 0.69 0.85 Industry 0.79 0.88 Financial 0.67 0.94 50 Most Active Companies 0.65 0.85 Basic Metals Table (2) Evaluation of the accuracy of the LSTM model for training and testing datasets by Index Testing Training Indices 0.81 0.85 Total 0.77 0.88 Industry 0.80 0.84 Financial 0.75 0.94 50 Most Active Companies 0.73 0.87 Basic Metals Discussion and conclusion The results demonstrate that the proposed EWS achieves predictive accuracy ranging from 73% to 81% across all selected indices, with the highest performance on the Total Index (81%) and the lowest on the Basic Metals Index (73%). A comparative analysis of model performance confirms that the LSTM model consistently outperforms the LR benchmark across all indices. This superiority is particularly evident in the case of the Total Index, where the LSTM model's accuracy of 81% represents a 12-percentage-point improvement over the LR model's 69%. Similarly, the accuracy improved by 8 percentage points for the Industrial, 50 Most Active Companies, and Basic Metals indices, while a marginal improvement of 1 percentage point was observed for the Financial Index. In terms of predictive quality, as evaluated by the AUC-ROC and confusion matrices, the findings present a nuanced picture. The AUC-ROC metrics indicate that the performance of the LSTM and LR models is somewhat comparable. In contrast, the evaluation based on confusion matrices reveals a substantially superior performance for the LSTM model, particularly in its ability to correctly identify true positives. هدف اصلی این پژوهش، طراحی سیستم هشدار سریع با مدل شبکه‎‌های عصبی برگشتی رهیافت LSTM (یادگیری عمیق) است که قادر به پیش‎‌بینی به‎‌هنگامِ حباب‎‌های قیمتی در بورس تهران باشد. هدف دیگر، مقایسۀ دقت و کیفیت نتایج با مدل LR (یادگیری ماشین) با استفاده از شاخص‎‌های ارزیابی مساحت زیر منحنی و ماتریس درهم‎‌ریختگی است. عملکرد این سیستم بر روی پنج شاخص‎‌ منتخب بورس تهران آزمایش شد. ابتدا داده‎‌های ماهانۀ شاخص‎‌های منتخب طی سال‎‌های 1381 تا 1402 استخراج‎‌ و سپس دوره‎‌های حبابی توسط آزمون GSADF شناسایی و متغیر مجازی حباب ایجاد شد. سپس با لحاظ تغییرات قیمتی (بازده لگاریتمی) مجموعه‎‌ای از شاخص‎‌های هشدار‌دهنده، مدل‎‌سازی پیش‎‌بینی متغیر حباب انجام شد. سیستم مدنظر با رویکرد یادگیری عمیق و با دقت حدود 73% تا 81% طراحی شدکه بیشترین دقت پیش‎‌بینی مربوط به شاخص کل با 81% و کمترین مربوط به شاخص فلزات اساسی با 73% بود. با مقایسۀ نتایج مدل LSTM با مدل LR ملاحظه شد که دقت پیش‎‌بینی در تمامی شاخص‎‌های منتخب بهبود داشته است. مقایسۀ نتایج شاخص‌های ارزیابی نشان از عملکرد مطلوب مدل LSTM در مقایسه با مدل LR می‎‌‎‌دهد. براساس بررسی‎‌ها، سیستم هشدار سریعی با هدف پیش‎‌بینی حباب‌های قیمتی و با استفاده از یادگیری عمیق طراحی نشده است.

https://amf.ui.ac.ir/article_29846_df89e86f7b1a65c9b6820cb92fb9d95a.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 14 3 2026 09 23 The Transmission of Macroeconomic Risk to Sukuk Returns in Iran انتقال ریسک از عوامل کلان اقتصادی به بازده ‌صکوک در ایران 59 82 29834 10.22108/amf.2025.144671.1970 FA منیژه رامشه دانشیار، گروه حسابداری، دانشکده علوم اقتصادی و اداری، دانشگاه قم، قم، ایران 0000-0001-9265-8819 وحید امیدی استادیار، گروه اقتصاد، دانشکده علوم اقتصادی و اداری، دانشگاه قم، قم، ایران امیر زلقی دانشجوی کارشناسی ارشد، گروه حسابداری، دانشکده علوم اقتصادی و اداری، دانشگاه قم، قم، ایران Journal Article 2025 03 14 This study explores the transmission of risk from key macroeconomic variables, specifically the exchange rate, oil price, inflation, and liquidity, to Sukuk returns, employing the Time-Varying Parameter Vector Autoregression (TVP-VAR) model over the period 2014 to 2022. The findings indicate that these variables play distinct roles in the spillover of risk to Sukuk instruments. Exchange rate and oil price are identified as the primary transmitters of risk, exerting a persistent influence on Sukuk returns, particularly those of non-governmental Sukuk. Inflation also demonstrates a significant impact, underscoring its critical role in the risk transmission process. Across all time periods, both governmental and non-governmental Sukuk are consistently characterized as recipients of risk from macroeconomic shocks. Short-term over-the-counter (OTC) Sukuk are especially susceptible to fluctuations in exchange rate and inflation. These results suggest that policymakers should prioritize the stabilization of the exchange rate and oil price volatility to mitigate their adverse effects on financial markets. Furthermore, the study corroborates previous research, reaffirming the strong influence of exchange rate and oil price dynamics on Sukuk performance across different timeframes. In light of these findings, economic strategies targeting these macroeconomic variables should be given heightened attention within national financial and economic policy frameworks to reduce investment risk in Sukuk and enhance resilience to economic shocks. Keywords: Sukuk, Exchange Rate, Oil Prices, Inflation, TVP-VAR. JEL Classification: E44, P44, C58 Introduction Financial markets are a core pillar of national economies, playing a crucial role in resource allocation and asset pricing. In conventional financial markets, bond issuance serves as a primary mechanism for financing. Since traditional bonds are based on interest-bearing loans, they are considered usury and are prohibited in Islam. Consequently, these instruments cannot be used for financing in Islamic economies. In response, Islamic countries, drawing on the knowledge of scholars, have issued Sharia-compliant securities to support interest-free banking. Islamic financial instruments, particularly Sukuk, have attracted substantial interest from a broad range of investors. Given the increasing prominence of Sukuk in Iran’s financial market, the yields on these securities are influenced by a set of macroeconomic factors. Variables such as exchange rates, oil prices, liquidity, and inflation can affect yield levels and the risk structure of these securities through various channels (Umar et al., 2023). The importance of this issue extends beyond yield fluctuations; shocks and volatility in these macroeconomic variables can be transmitted to the sukuk market, altering its risk dynamics. In the financial literature, this process is referred to as risk transmission and can exhibit asymmetry, dynamics, and time-varying behavior (Billah et al., 2022; Samitas et al., 2021). Thus, the research hypotheses specify the intensity of risk transmission from each macroeconomic factor, namely, the percentage changes in exchange rates, oil prices, liquidity, and inflation, to Sukuk returns. Methods and Materials This study investigates risk transmission and its intensity between macroeconomic factors, specifically, the percentage changes in oil price, exchange rate, liquidity, and inflation, and sukuk returns, including total Farabourse sukuk, Farabourse government bonds, Farabourse non-government bonds, and Farabourse short-term bonds. Monthly data for the macroeconomic variables for the period 2014–2022 were obtained from the Central Bank website. Sukuk returns comprise two components: price return and coupon (interest) return. The price return reflects changes in the market price of the sukuk, while the coupon return corresponds to the interest portion of the debt instrument since the last coupon payment. Information regarding debt security indices was extracted from the Iran Fara Bourse website. This study utilizes the methodology introduced by Balcilar et al. (2021), which is an improved version of the approach developed by Antonakakis et al. (2020). The extended connectedness framework proposed by Balcilar et al. (2021) offers several key advantages over the previous method (Antonakakis et al., 2020). In addition to capturing dynamic interconnections, this framework allows for a more precise analysis of net directional linkages within the connectedness structure. While Anton's approach (Antonakakis et al., 2020) relies on fixed parameters and a general framework for dynamic connectedness analysis, the technique developed by Balcilar et al. (2021) yields more accurate and flexible results with less sensitivity to outliers. These features make it a more robust tool for identifying shock transmission in complex financial and economic networks. Findings The findings indicate that the oil variable emerges as the primary risk transmitter within the entire network, encompassing all variables considered in the study. The total return of sukuk throughout the study period has acted as a risk receiver from oil. Oil transmitted risk to non-governmental sukuk with high intensity over a short interval. The transmission of risk from oil to short-term sukuk remained stable and constant across the entire study period, without notable changes. The intensity of risk transmission from oil to governmental sukuk was moderate for roughly half of the study period, with a brief period characterized by an increased intensity of transmission. The exchange rate stands as one of the most influential nodes for risk transfer within the network; while it is influenced by oil, it also propagates substantial risk to other variables, including liquidity, inflation, and, in particular, the sukuk market. Liquidity plays a dual role: it is influenced by oil, the exchange rate, and inflation, yet it also acts as a key conduit for transferring risk to Sukuk. The transmission of risk from liquidity to the total return of Sukuk persisted across the entire study period. In most subperiods, the transfer of risk from liquidity to total Sukuk and to non-governmental Sukuk remained stable. The short-term Sukuk segment experienced the greatest impact from liquidity, but only during a brief interval. Government Sukuk exhibited the highest sensitivity to liquidity in a short period; however, in most periods examined, this susceptibility declined. Inflation is typically viewed as a recipient of risk from oil and the exchange rate; however, it has played a more active role with respect to Sukuk. In most periods studied, the risk transfer from inflation to the total return of Sukuk remained stable. The risk transfer from inflation to the return of non-governmental Sukuk was stable for a short period but exhibited high intensity for most of the study horizon. In the short term, the risk transfer from inflation to short-term Sukuk peaked, while in the medium term, it trended downward. From the medium term onward, a pronounced decline in the risk transfer from inflation to governmental Sukuk is observed. Discussion and Conclusion Based on the findings regarding the impact of macroeconomic factors on sukuk returns, the government, to improve its financing conditions, should foster stability in the sukuk market and reduce investor risk within this market. The same applies to corporate financing. The Central Bank, through appropriate monetary policy measures, should aim to stabilize macroeconomic variables such as inflation and exchange rates, to reduce the volatility of sukuk returns. Given the findings on risk transmission from oil price changes to sukuk returns and the country’s reliance on oil revenues, policymakers are advised to pursue diversification of government revenue sources beyond oil, for example, through tax instruments, to enhance stability in the sukuk market. Enhancing transparency in fiscal and monetary policies and disseminating accurate information can help investors better anticipate developments and reduce uncertainty, thereby contributing to lower sukuk return volatility. این پژوهش انتقال ریسک از متغیرهای کلان اقتصادی شامل نرخ ارز، قیمت نفت، تورم و نقدینگی را به بازده صکوک با استفاده از الگوی TVP-VAR در دورۀ زمانی 1393 تا 1401 بررسی می‌کند. نتایج نشان می‌دهد که این متغیرها نقش‌های متفاوتی در سرریز ریسک به صکوک ایفا کرده‌اند. نرخ ارز و قیمت نفت به‌عنوان متغیرهای اصلی ارسال‌کنندۀ ریسک به صکوک شناسایی شده‌ است، درحالی‌که صکوک به‌ویژه صکوک غیردولتی به‌طور مداوم از این متغیرها تأثیر پذیرفته است. تورم تأثیر چشمگیری بر صکوک دارد که نشان‌دهندۀ نقش مهم آن در انتقال ریسک است. در تمامی دوره‌های زمانی، صکوک دولتی و غیردولتی به‌عنوان دریافت‌کنندۀ ریسک از سایر متغیرهای کلان معرفی شده‌اند. صکوک کوتاه‌مدت فرابورس به‌شدت متأثر از نوسانات نرخ ارز و تورم قرار دارد. یافته‌ها نشان می‌دهد که سیاست‌گذاران باید بر کنترل نوسانات نرخ ارز و قیمت نفت تمرکز کنند تا از اثرات منفی آن‌ها بر بازارهای مالی جلوگیری شود. این پژوهش یافته‌های مطالعات پیشین را تأیید می‌کند که نشان‌دهندۀ تأثیر قوی نرخ ارز و قیمت نفت بر بازدۀ صکوک در دوره‌های مختلف است. براساس این نتایج، باید به راهبردهای اقتصادی مرتبط با این متغیرهای کلان به‌طور ویژه در سیاست‌گذاری‌های مالی و اقتصادی کشور توجه شود تا میزان ریسک سرمایه‌گذاری در صکوک کاهش یابد و شوک‌های اقتصادی به‌خوبی مدیریت شود.

https://amf.ui.ac.ir/article_29834_8836417536766d9202cf7e0c97d04918.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 14 3 2026 09 23 Managerial Empire-Building in the Shadow of Tax Avoidance: Is Financial Constraints a Hindrance or a Driver? امپراتوری‌سازی مدیریتی در سایۀ اجتناب مالیاتی؛ آیا محدودیت‌های مالی مانع یا محرک هستند؟ 83 98 29900 10.22108/amf.2025.145085.1980 FA مهدی علی بالائی دکتری تخصصی، گروه حسابداری، واحد اصفهان (خوراسگان)، دانشگاه آزاد اسلامی، اصفهان، ایران Journal Article 2025 04 30 Firms experiencing financial constraints, often a consequence of weak performance, may engage in tax avoidance to alleviate funding pressures. While this practice can mitigate financial strain, it may also create opportunities for managers to pursue self-interested objectives, such as managerial empire-building, at the expense of shareholder wealth maximization. This study examines the effect of tax avoidance on managerial empire-building and investigates the moderating role of financial constraints in this relationship. Using panel data from 105 firms listed on the Tehran Stock Exchange over the period 2014–2022, we test our hypotheses using multiple regression analysis. The results indicate that tax avoidance has a positive and significant effect on empire-building. Moreover, financial constraints strengthen this relationship, implying that managers under greater funding pressure are more likely to divert tax savings toward opportunistic empire-building activities. These findings contribute to the corporate finance and governance literature by elucidating how a firm's financial environment conditions the consequences of tax avoidance. The study highlights the dual nature of tax avoidance: though it can serve as a source of internal financing, it may also enable value-destroying managerial behavior. Consequently, this research offers valuable insights for policymakers, shareholders, and boards of directors aiming to enhance oversight and ensure that tax savings are allocated to value-enhancing investments. Keywords: Tax Avoidance, Management Empire Building, Financial Constraints. JEL Classification: H26, G32, G01 Introduction Managerial empire-building occurs when managers expand firms beyond their optimal size to pursue self-serving behavior, such as prestige, power, and excessive compensation (Bragoli, 2021; Hope & Thomas, 2008). This behavior often leads to overinvestment, inefficient asset growth, and value-destroying acquisitions. It can also diminish financial reporting quality, as managers may selectively conceal information to obscure the outcomes of such activities (Young et al., 2014; Weiskirchner-Merten, 2023). In parallel, tax avoidance is a prevalent corporate strategy aimed at reducing costs and enhancing liquidity (Pratama, 2018). While potentially increasing shareholder wealth in the short term, prior research suggests that in the absence of strong monitoring, managers may divert tax savings toward private gains, including empire-building (Desai & Dharmapala, 2006, 2009; Atwood & Lewellen, 2019). Evidence indicates that weak governance structures amplify these agency problems, ultimately eroding firm value (Hanlon & Heitzman, 2010; Shams et al., 2022; Sadeghi et al., 2023). Empirical studies further document a positive association between tax avoidance and the inefficient expansion of firm assets (Desai et al., 2007; Chen et al., 2010). More recent scholarship emphasizes the moderating role of financial constraints, positing that when external financing is limited, tax avoidance serves as an alternative internal funding channel, thereby exacerbating managerial opportunism (Dhaliwal et al., 2004; Edwards et al., 2013). Building on this theoretical foundation, the present study tests the following hypotheses: 1) Tax avoidance has a positive effect on managerial empire-building and 2) Financial constraints strengthen the positive relationship between tax avoidance and managerial empire-building. The findings are expected to offer valuable insights for shareholders, regulators, and policymakers by elucidating how the interplay between tax avoidance and empire-building distorts corporate resource allocation and potentially undermines broader stakeholder interests. Materials & Methods The analysis utilizes panel data from companies listed on the Tehran Stock Exchange (TSE) over the period 2014-2015. The initial sample was subjected to standard screening procedures, resulting in a final balanced panel of 105 firms. All data were processed and analyzed using EViews 10. The dependent variable, managerial empire-building, is measured using a composite index constructed from five components—acquisitions, consolidations, capital expenditure growth, total asset growth, and tangible fixed asset growth—this index follows the methodologies established in prior literature (Chhaochharia et al., 2012; Levi et al., 2014; Gul et al., 2020; Shams et al., 2022). The index is calculated according to Equation (1), which normalizes the value to a range between zero and one. (1) In this index, the numerator represents the count of conditions satisfied by the firm (each coded as 1), and the denominator is the total number of conditions (five), yielding a normalized score ranging from 0 to 1. The independent variable, tax avoidance (TAX_AVOID), is measured as the ratio of cash tax payments to pre-tax book income, multiplied by –1 (Safari Graili & Pudine, 2016; Lee & Bose, 2021). This measure, often referred to as the cash effective tax rate (ETR), results in higher values indicating a greater degree of tax avoidance. Financial constraints (FC) are measured using the Z-score model developed by BadavarNahandi and Darkhor (2013), specified as follows: (2) This version is efficient and commonly used in high-impact papers. The model incorporates several control variables to mitigate omitted variable bias, including cash holdings, leverage, profitability (return on assets, ROA), firm size, market-to-book ratio, sales growth, firm age, institutional ownership, and CEO ability. CEO ability is estimated using the methodology developed by Demerjian et al. (2012). To test the first hypothesis, the following regression model is estimated: (3) To test the second hypothesis, the interaction term TAX_AVOID × Z-score is added: (4) where a positive and significant β3 indicates that financial constraints strengthen the effect of tax avoidance on managerial empire-building. Findings The descriptive statistics indicate a mean tax avoidance value of –0.0831, suggesting the data are concentrated around this point. Diagnostic tests confirmed the presence of autocorrelation and heteroskedasticity; consequently, the models were estimated using AR(1) and Generalized Least Squares (GLS) methods to correct for these issues. The results indicate that tax avoidance exerts a positive and statistically significant effect on managerial empire-building (β = 0.224, p = 0.009) and supports the firs hypothesis. Among the control variables, cash holdings, financial leverage, profitability, firm size, and sales growth also showed positive and significant associations with empire-building. In contrast, the market-to-book ratio and firm age were negatively and significantly related to the dependent variable. The effects of institutional ownership and CEO ability were found to be statistically insignificant. The model demonstrates strong explanatory power, with an R-squared of 0.64. Regarding the second hypothesis, the analysis reveals that the interaction term between tax avoidance and financial constraints has a positive and significant effect on managerial empire-building (β = 0.234, p = 0.023). This finding suggests that financial constraints amplify the positive effect of tax avoidance on empire-building. Furthermore, a Wald test confirms that the coefficients for the main effect of tax avoidance and the interaction term are jointly significant and statistically distinct from one another (p = 0.045), thereby validating the moderating role of financial constraints. In summary, the results underscore that tax avoidance, by providing internal financial resources, strengthens managers' propensity for empire-building. This relationship is significantly intensified when firms face financial constraints. Discussion and conclusion This study provides empirical evidence that tax avoidance has a positive and significant effect on managerial empire-building, thereby confirming its first hypothesis. This finding aligns with prior research (Sadeghi et al., 2023; Shams et al., 2022) and is well-explained by agency theory. The theory posits that the separation of ownership and control creates opportunities for managers to act in their own self-interest. In this context, tax avoidance serves as a mechanism to generate discretionary resources, which managers may then divert to pursue personal benefits—such as increased compensation, power, and prestige—through empire-building, rather than maximizing shareholder wealth. Furthermore, the results demonstrate that financial constraints significantly strengthen the positive relationship between tax avoidance and empire-building, thus supporting the second hypothesis. This indicates that the pressure of limited external financing exacerbates managerial opportunism, a finding consistent with extant empirical literature. These findings yield several important implications for corporate governance and investment. To mitigate these agency costs, shareholders and boards of directors should enhance monitoring mechanisms and redesign executive compensation contracts to better align managerial incentives with long-term value creation. This could involve appointing independent board members and, in egregious cases, replacing CEOs who persistently engage in value-destroying expansion. For investors, these results underscore the need for vigilant scrutiny of managerial behavior, particularly in firms with weak governance structures. When assessing corporate strategy, investors should distinguish between diversifications that create genuine synergies and those that merely reflect empire-building. In the latter case, where diversification is unrelated and value-destroying, divestiture may be a preferable strategy, as shareholders can achieve diversification more efficiently through their own portfolio choices. هنگامی که به دلیل عملکرد ضعیف شرکت دسترسی به منابع مالی محدود شده باشد، اجتناب مالیاتی می‌تواند نقش مهمی در جهت تأمین منابع مالی ایفا کند. در این بین مدیران فرصت‌طلب می‌توانند از صرفه‌جویی‌های مالیاتی برای ساخت امپراتوری استفاده ‌کنند. هدف اصلی این پژوهش بررسی تأثیر اجتناب مالیاتی بر ساخت امپراتوری مدیریتی باتوجه‌به نقش محدودیت‌های مالی است. نمونۀ آماری این پژوهش شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران و شامل داده‌های 105 شرکت برای دورۀ 9سالۀ 1401-1393 است. برای تجزیه‌وتحلیل داده‌ها و آزمون فرضیه‌‌ها از مدل‌های رگرسیون چندگانه به روش‌ داده‌های ترکیبی استفاده شده است. نتایج نشان داد که اجتناب مالیاتی تأثیر مثبتی بر ساخت امپراتوری مدیریتی دارد و محدودیت‌های مالی این اثر را تقویت می‌کند.

https://amf.ui.ac.ir/article_29900_9cad89b770886deed6a4bc81f96b88a2.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 14 3 2026 09 23 The Term Structure of Investor Sentiment and Stock Return ساختار زمانی احساسات سرمایه‌گذار و بازده سهام 99 124 29940 10.22108/amf.2025.145197.1987 FA مریم دولو گروه مدیریت مالی و بیمه، دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران 0000-0003-3321-1165 محمد رضا فقیهی حبیب آبادی گروه آمار، دانشکده علوم ریاضی، دانشگاه شهید بهشتی، تهران، ایران نازنین کوکانی گروه مدیریت مالی و بیمه، دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران Journal Article 2025 05 16 This study investigates the impact of investor sentiment on stock returns across short-term daily, weekly, and monthly horizons—namely the sentiment term structure. While prior literature establishes a link between sentiment and stock price volatility, empirical evidence on the role of the time horizon remains limited and conflicting. We test the effect of investor sentiment on excess stock returns at these three frequencies using panel regression, controlling for market excess returns, size, and value factors. Investor sentiment is measured through four indirect proxies, synthesized into composite indices using both Principal Component Analysis (PCA) and the Kalman filter. The results indicate that investor sentiment exerts a significant yet temporally decaying influence on stock returns; the magnitude of the effect diminishes from the daily to the weekly and monthly horizons, revealing a downward-sloping term structure. This finding is robust, as it holds for both the PCA- and Kalman filter-based sentiment indices. To our knowledge, this is the first study to systematically examine the differential impact of investor sentiment across these high-frequency horizons and the first to empirically validate its temporal structure using both of these methodological approaches. Key words: Kalman Filter, Principal Component Analysis, Stock Return, Term Structure of Investors Sentiment JEL Classification: C22, C38, G12, G41 Introduction Classical finance theories are predicated on the assumption of rational investors, asserting that stock prices are determined by fundamental factors while largely neglecting the influence of investor sentiment. However, this paradigm is challenged by historical market anomalies such as the 1929 Great Depression, Black Monday in 1987, and the dot-com bubble of the 1990s. Corroborating these events, a growing body of empirical research confirms that investor sentiment significantly contributes to stock price volatility (Baker & Wurgler, 2007; Kim & Ha, 2010; Kumar & Lee, 2006; Frazzini & Lamont, 2008; Antoniou et al., 2013). Despite this established connection, the majority of empirical studies examine sentiment effects within a single, static time horizon. A critical gap exists, as emerging evidence suggests that the impact of sentiment is not uniform but varies across different timeframes (Li, 2020; Kim & Ryu, 2021). The underlying rationale is that as new information emerges over weekly or monthly periods, initial emotional reactions among investors are corrected, and stock prices tend to revert toward their intrinsic values, highlighting the time-dependent nature of sentiment effects. This study, therefore, investigates the impact of investor sentiment on stock returns across daily, weekly, and monthly horizons—a relationship we term the temporal structure of sentiment. Elucidating this dynamic is crucial for informing sound investment decisions, effective policy-making, and robust risk management practices. To accurately measure this latent construct, we rely on indirect proxies derived from financial and economic variables. Recognizing that any single variable captures both sentiment and unrelated noise, we synthesize multiple indicators to construct a more efficient and robust measure of unobservable investor sentiment (Baker & Wurgler, 2006). Specifically, this study employs two distinct methodologies to create composite sentiment indices: Principal Component Analysis (PCA), which extracts the common variation from a set of proxies (Berger & Turtle, 2011; Huang et al., 2014; Kamath et al., 2024), and the Kalman filter, a state-space technique designed to process all available information from the variables while optimally minimizing noise and estimation errors. Materials and Methods To test the first hypothesis concerning the impact of investor sentiment on excess stock returns across daily, weekly, and monthly horizons, a panel regression was employed, following Li (2020). Equations (1) through (3) were specified for this purpose and were estimated separately for the sentiment indices derived from Principal Component Analysis (PCA) and the Kalman filter. (1) (2) (3) where is investor sentiment change, is the size factor, is the book-to-market factor, is risk-free rate, and is market return at time t and i refers to ith firm stock. To test the second hypothesis—that the influence of investor sentiment diminishes over longer time horizons—the sentiment coefficients (β) estimated from the panel regressions were annualized using Equation (4). These annualized coefficients were then systematically compared across the daily, weekly, and monthly frequencies. (4) Where denotes the annual sentiment coefficient, represents the sentiment coefficient corresponding to each frequency, and the constant 250 indicates the assumed number of trading days in a year. Stock return was calculated as the natural logarithm of the difference in adjusted closing prices, which account for dividends and stock splits. The market risk premium was defined as the difference between the market return and the risk-free rate, the latter proxied by Central Bank bond yields. The market return was computed as the log return of the Tehran Stock Exchange (TSE) index. The size (SMB) and the value factor (HML) factors were constructed following the methodology of Fama and French (1992). The composite sentiment index was constructed for daily, weekly, and monthly frequencies using four variables and two methods of PCA and Kalman filter. PCA extracts common components assumed to capture investor sentiment, while the Kalman filter processes all observed information to estimate sentiment while minimizing noise (Brown & Cliff, 2004; Li, 2020). Sentiment proxies are measured as below: a) Adjusted turnover rate – reflects changes in stock liquidity due to investor sentiment (Baker & Stein, 2004). (5) In which, Rit represents the return of stock i at time t, VOLit denotes the trading volume (measured in number of shares) for firm i at time t, and Shares Outstanding refers to the total shares outstanding for firm i at time t. b) Buy–sell imbalance – captures net retail demand for a stock at a given time. (6) In which denotes the purchase volume of stock i on day j during period t, and represents the selling volume of stock i on day j during period t. c) Trading volume – higher traded value indicates elevated investor sentiment (Li & Yang, 2017). (7) In which represents the total number of shares of stock i traded during period t, and denotes the closing price of stock i at time t. d) Psychological line index – measures the proportion of positive trading days, indicating general market sentiment toward a stock. (8) In which indicates the number of trading days during period t on which the closing price of stock i exceeds that of the previous day, and represents the total number of trading days for stock i during period t. Findings To assess the impact of investor sentiment on excess stock returns, we estimated panel regressions using a sequence of models: a single-factor model (investor sentiment), a two-factor model (adding the market excess return), and a multi-factor model (further incorporating the size and value factors). The results for the PCA-based sentiment index are reported in Table 1. Table (1) Results of the effect of investor sentiment (PCA) on excess returns Variable Frequency Daily Weekly Monthly a Intercept 0/0027*** 0/0084*** 0/0258*** Investor Sentiment 0/0099*** 0/0249*** 0/0548*** Adjusted R-squared 0/1536 0/2292 0/2485 Annual Coefficient of Investor Sentiment 9/7420 2/2722 0/9019 b Intercept 0/0014*** 0/0044*** 0/1070*** Investor Sentiment 0/0093*** 0/0224*** 0/0484*** Excess Market Return 0/7290*** 0/6420*** 0/6140*** Adjusted R-squared 0/2614 0/3340 0/3628 Annual Coefficient of Investor Sentiment 8/3086 1/9088 0/7674 c Intercept 0/0010*** 0/0037*** 0/0048*** Investor Sentiment 0/0091*** 0/0223*** 0/0440*** Excess Market Return 0/8994*** 0/6906*** 0/7949*** Size Factor (SMB) 0/0486*** 0/2648*** 0/5311*** Value Factor (HML) 0/0270*** -0/0202** 0/0054 Adjusted R-squared 0/2925 0/3492 0/4286 Annual Coefficient of Investor Sentiment 7/8744 1/8951 0/6801 The results indicate a positive and statistically significant effect of investor sentiment on excess stock returns across all examined frequencies—daily, weekly, and monthly. Notably, the adjusted R² values exhibit an increasing trend from daily to monthly horizons, suggesting that the explanatory power of sentiment strengthens over longer timeframes. This finding is consistent with the literature, including Andleeb (2023) and McClure et al. (2004), which posits that investor sentiment exerts a more pronounced influence on short-term investment decisions, with this effect gradually dissipating as the investment horizon extends. Crucially, the persistence of a significant sentiment effect in our multi-factor models, which control for other systematic risks, indicates that its impact on returns is distinct and not subsumed by established risk factors. To ensure robustness, the analysis was replicated using the Kalman filter-based sentiment index. As summarized in Table 2, the results remain qualitatively unchanged, thereby reinforcing the primary conclusions. Table 2: Effect of Investor Sentiment (Kalman Filter) on Excess Return Variable Frequency Daily Weekly Monthly a Intercept 0/0007*** 0/0033*** 0/0155*** Investor Sentiment 0/0023*** 0/0092*** 0/0236*** Adjusted R-squared 0/0531 0/1483 0/2119 Annual Coefficient of Investor Sentiment 0/7396 0/5549 0/3245 b Intercept 0/0002*** 0/0007*** 0/0033*** Investor Sentiment 0/0023*** 0/0087*** 0/0218*** Excess Market Return 0/5878*** 0/5929*** 0/6084*** Adjusted R-squared 0/1429 0/2550 0/3565 Annual Coefficient of Investor Sentiment 0/7396 0/5182 0/2967 c Intercept 0/0000 0/0003* -0/0010 Investor Sentiment 0/0023*** 0/0085*** 0/0205*** Excess Market Return 0/7609*** 0/6395*** 0/7630*** Size Factor (SMB) 0/4387*** 0/2389*** 0/4806*** Value Factor (HML) 0/0315*** -0/0084 0/0051 Adjusted R-squared 0/1770 0/2709 0/4152 Annual Coefficient of Investor Sentiment 0/7396 0/5037 0/2770 Consistent with the primary results, the Kalman filter-based sentiment index also demonstrates a positive and statistically significant influence on stock return across all observed frequencies. However, a key finding emerges when examining the annualized sentiment coefficients: their magnitude displays a monotonic decline as the investment horizon extends from daily to monthly. This pattern indicates a decaying term structure for investor sentiment, where its pricing effect attenuates over longer periods. This result robustly confirms that the impact of investor sentiment on excess returns follows a consistently downward-sloping term structure. Discussion and conclusion This study provides robust evidence of a direct and significant impact of investor sentiment on excess stock return across daily, weekly, and monthly investment horizons. These findings align with a growing body of literature that underscores the importance of sentiment in short-term price formation (e.g., Andleeb, 2023; Seok et al., 2018; Ryu et al., 2016; Dai, 2025). A central finding is the decaying influence of investor sentiment as the observation horizon extends from daily to monthly data, revealing a consistently downward-sloping term structure. This pattern suggests that irrational behavioral factors disproportionately drive short-term investment decisions, whereas their influence wanes over longer periods. The results corroborate the findings of Yang & Gao (2014) and extend the work of Li (2020) to the context of the Tehran Stock Exchange, demonstrating that short-term waves of investor optimism or pessimism are ultimately transient. Consequently, investor sentiment appears to be a key driver of short-term asset mispricing, generating excess returns that are subsequently corrected as prices converge toward their fundamental values over the long run. Crucially, this downward term structure is not an artifact of measurement, as it holds consistently for sentiment indices constructed using both Principal Component Analysis and the Kalman filter. This methodological robustness strongly confirms the time-dependent nature of sentiment effects, as theorized by Fu (2024). Furthermore, the persistent explanatory power of sentiment even after controlling for market, size, and value factors—a consistency also noted by Brown & Cliff (2004)—indicates that the informational content of investor sentiment captures dimensions of risk and return distinct from those in traditional asset-pricing models. پژوهش‌های اخیر نشان می‌دهد که بخشی از نوسان قیمت سهام متأثر از احساسات سرمایه‌گذار است. از یک سو باوجود شواهد تجربی متناقض دربارۀ اثر مذکور و ازسوی دیگر مطالعات محدودی که به نقش افق زمانی در رابطۀ احساسات و بازدۀ سهام توجه می‌کند، هدف این پژوهش بررسی تأثیر احساسات سرمایه‌گذار بر بازدۀ سهام در سه بازۀ زمانی کوتاه‌مدت روزانه، هفتگی و ماهانه است (ساختار زمانی احساسات). اثر احساسات سرمایه‌گذار بر بازدۀ اضافی سهام در سه تواتر زمانی روزانه، هفتگی و ماهانه با استفاده از رگرسیون داده‌‌های ترکیبی و کنترل اثر بازدۀ اضافی بازار، عوامل ارزش و اندازه آزمون و با یکدیگر مقایسه شده است. شاخص احساسات سرمایه‌گذار با استفاده از چهار سنجۀ غیرمستقیم «نرخ تعدیل‌شدۀ گردش سهام»، «عدم‌تعادل خرید-فروش»، «مبلغ معامله» و «شاخص خط روان‌شناختی» و دو روش «تحلیل مؤلفه‌های اصلی» و «فیلتر کالمن» اندازه‌گیری شده است. یافته‌ها حاکی‌از تأثیر احساسات سرمایه‌گذار بر بازدۀ سهام است؛ اما شدت این تأثیر در گذر زمان از داده‌های روزانه به هفتگی و ماهانه، تقلیل یافته و ساختار زمانی احساسات سرمایه‌گذار تابع نزولی از زمان است. به سخن دقیق‌تر، با گذشت زمان همراه با افشای اطلاعات بیشتر و کاهش عدم‌اطمینان، نقش احساسات در تصمیم‌گیری سرمایه‌گذاران کاهش می‌یابد و عوامل فراگیر ریسک قادر به پوشش محتوای اطلاعاتی احساسات سرمایه‌گذار نیست. نتایج حاصل از مقایسۀ اثر احساسات بر بازدۀ سهام با استفاده از سنجۀ مبتنی بر روش‌های «فیلتر کالمن» با نتایج استفاده از سنجۀ «تحلیل مؤلفه‌های اصلی» همسو است. پژوهش حاضر برای نخستین بار به واکاوی تأثیر متفاوت احساسات سرمایه‌گذار بر بازدۀ سهام در سه بازۀ روزانه، هفتگی و ماهانه می‌پردازد و ساختار زمانی احساسات سرمایه‌گذار را می‌آزماید. در پژوهش‌های پیشین عمدتاً از شاخص ترکیبی احساسات مبتنی بر روش «تحلیل مؤلفه‌های اصلی» استفاده شده است. در این پژوهش برای اولین بار در بورس اوراق بهادار تهران از روش «فیلتر کالمن» برای ساخت شاخص احساسات استفاده شده و ساختار زمانی احساسات براساس دو روش «فیلتر کالمن» و «تحلیل مؤلفه‌های اصلی» آزمون شده است.

https://amf.ui.ac.ir/article_29940_364e8c24a043287817aabf5497b220fe.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 14 3 2026 09 23 Investigating the Role of Financial Information Readability on Obtaining Credit Financing with Emphasis on the Effectiveness of Competition in the Product Market بررسی نقش خوانایی اطلاعات مالی بر دستیابی به تأمین مالی اعتباری با تأکید بر اثربخشی رقابت در بازار محصول 125 142 29937 10.22108/amf.2025.145922.2002 FA عبدالرسول رحمانیان کوشککی استادیار، گروه حسابداری، دانشگاه پیام نور، تهران، ایران گلنار بلوچی کارشناسی ارشد، گروه حسابداری، دانشگاه پیام نور، تهران، ایران Journal Article 2025 07 11 This study examines the impact of financial statements readability on corporate credit financing, with a specific focus on the moderating role of product market competition. Employing a causal research design, the analysis uses a sample of 141 companies listed on the Tehran Stock Exchange over the 10-year period from 2014 to 2024. The findings indicate a significant positive relationship between financial statement readability and access to credit. Furthermore, product market competition is shown to negatively moderate this relationship. Specifically, heightened competition attenuates the positive effect of readability, thereby constraining firms' ability to secure credit financing. These results underscore how competitive market forces can limit financial flexibility, even for firms with transparent disclosures. Keywords: Readability of Financial Information, Credit Financing, Product Market Competition, Stock Exchange JEL Classification: G30, G38, G40 Introduction Competitiveness denotes a firm's capacity to maintain its market position, protect corporate assets, ensure returns on investment, and safeguard future employment. Given this scope, competition exerts a substantial influence on corporate activities and strategic decisions (Khoddadi & Rashidi Baghi, 2014). A principal metric for evaluating market competition, monopoly power, and industry structure is the degree of concentration. Market concentration describes the distribution of market share among firms within an industry, effectively indicating the extent to which a small number of firms dominate total industry output. Consequently, industries with fewer participants typically exhibit higher concentration levels. An analysis of firms listed on the Tehran Stock Exchange (TSE) confirms this pattern; in major sectors such as petrochemicals, steel, automotive manufacturing, and financial intermediation, a limited number of large companies control the majority of industry sales, resulting in highly concentrated market structures. As noted in prior research, these dominant firms achieve superior sales revenues compared to their industry peers, a direct outcome of their market control (Khoddadeh Shamloo & Gharsi, 2018). Porter (1990) contends that product market competition shapes managerial decisions and is a critical determinant of corporate profitability. The competitive literature further posits that intense competition serves as an incentive for managerial efficiency, as competitive markets swiftly discipline underperformance. Thus, product market competition functions as an external governance mechanism, monitoring management and mitigating agency costs (Khoshkar & Farghani, 2020). Competitiveness can also be defined as an economic entity's ability to maintain or increase its share in international markets. A firm's sales volume is a reflection of its market influence; as such, companies are driven to preserve and expand their market share. This often leads to enhanced service quality for stakeholders—including the quality of disclosed financial information. Such improvements attract the attention of stakeholders and creditors, thereby indirectly influencing access to credit-based financing (Li et al., 2024). The research gap addressed by this study arises from the scarcity of comprehensive research that examines these factors in concert. Previous studies have predominantly investigated the impact of financial report readability on investor and creditor decisions in isolation. Financial information readability—defined as the ease with which financial statements can be understood—enhances transparency, reduces information asymmetry, and lowers financing costs. Simultaneously, the intensity of product market competition can alter managerial incentives and significantly influence financial performance and resource acquisition. The complex interplay between information readability and competitive market conditions remains a notable theoretical void. Furthermore, the role of product market competition in determining financial resource access establishes a foundation for improving credit acquisition capacity. Intensified competition encourages optimal resource utilization and fosters financial reporting transparency, which may, in turn, amplify the effect of financial information readability on credit financing. However, existing literature has inadequately explored the moderating role of product market competition, particularly within emerging markets characterized by unique institutional features. These considerations motivate the present study to address a critical scientific and practical gap by concurrently examining the roles of financial information readability and product market competition in corporate financing processes. Hypothesis 1: A significant relationship exists between financial information readability and access to credit-based financing. Hypothesis 2: Product market competition moderates the relationship between financial information readability and access to credit-based financing. Materials & Methods This study employs a dataset comprising firms listed on the Tehran Stock Exchange (TSE). The data were sourced from the CODAL and TSE official websites and analyzed using EViews 12 software. The sample includes all companies listed on the TSE. This sample was selected based on data accessibility, its direct relevance to the research context, and the availability of audited, reliable financial statements. Following the application of systematic exclusion criteria detailed in Table 1, the final sample consists of 141 companies observed over the 10-year period from 2014 to 2023, yielding a balanced panel of 1,410 firm-year observations. Findings Descriptive statistics are reported for the panel of 141 sample companies over the 10-year period from 2013 to 2022 (1,410 firm-year observations). The mean value for financial leverage is 0.53. The values of this parameter for firm size and return on assets are 1.72 and 0.15, respectively, with their standard deviations suggesting the dispersion around these means. The minimum and maximum values reported for each variable delineate their observed ranges. The diagnostic tests, summarized in Table 3, confirm the presence of cross-sectional dependence and serial correlation in the initial models. To address these issues, the models were estimated using the Generalized Least Squares (EGLS) method in EViews 12, which employs a robust variance-covariance matrix to correct for heteroskedasticity. Furthermore, the inclusion of an AR(1) term in the final model specification successfully mitigated the problem of serial autocorrelation. A Chow test, significant at the 5 percent level, supported the use of a panel data approach. Subsequently, a Hausman test, also significant at the 5 percent level, indicated that the fixed effects estimator was more appropriate than the random effects estimator for the final analysis. Based on the results, the financial information readability variable exhibits a positive and significant relationship with credit financing, with a coefficient of 0.54 (p < 0.01). Therefore, the first hypothesis is supported at the 1 percent significance level. The model demonstrates a high explanatory power, with an R-squared of 0.91, indicating that the independent and control variables account for 91 percent of the variation in the dependent variable. Furthermore, all variance inflation factor (VIF) values are below 5, confirming that multicollinearity is not a concern. The overall model fit is confirmed by the F-statistic, which is significant at the 1 percent level. The results for the second hypothesis are as follows. The interaction term between financial information readability and product market competition has a negative and significant coefficient of -0.46 (p < 0.01), indicating a negative moderating effect on credit financing. Thus, the second hypothesis is also supported at the 1 percent level. This finding suggests that increased product market competition attenuates the positive effect of financial readability on access to credit; in other words, competition acts as a moderating variable that weakens the benefit of readable disclosures. Qualitatively, this may be attributed to the heightened risk and uncertainty inherent in competitive markets. Intense competition pressures profitability and liquidity, potentially increasing lenders' perceived risk and caution in extending credit, thereby overshadowing the transparency benefits of readable reports. The model's R-squared is 0.90, the Durbin-Watson statistic is 1.89, and the VIF statistics remain below 5, collectively indicating a well-specified model with a strong fit, as confirmed by the significant F-statistic. Discussion and conclusion As mentioned before the primary objective of this study was to investigate the role of financial information readability in securing credit financing, with a specific emphasis on the moderating effect of product market competition. The novelty of this research lies in its integration of two significant domains—financial economics and industrial organization—which have seldom been examined in a simultaneous and interactive manner. By combining the concept of financial report readability, which underscores information transparency and comprehensibility, with the dynamics of product market competition, this study establishes a new framework for understanding corporate resource acquisition mechanisms. The focus on competition as a moderating variable constitutes the central innovation, demonstrating how market competition intensity can influence the relationship between financial information quality and access to credit. This approach contributes new knowledge to the field of corporate finance and offers insights for refining credit policies in emerging markets, thereby addressing a significant theoretical and practical gap. Specifically, this study breaks new ground by analyzing the interplay between financial readability and product market competition, factors that have previously been studied in isolation. By focusing on financial report readability as a mechanism for enhancing transparency and reducing information asymmetry, and by analyzing the moderating role of market competition, this study offers a novel perspective on the corporate financing process. Consequently, it addresses a critical void in the literature, highlighting the significant role of the interaction between information quality and market structure in improving firms' access to credit, particularly within emerging economies. The results from the first hypothesis confirm a significant positive relationship between financial information readability and trade credit financing. Specifically, when companies provide accurate, comprehensible, and unambiguous information in their financial statements, it serves as a positive signal to financial statement users. This signal assures creditors that the firm has not engaged in informational obfuscation and possesses a sound financial position capable of repaying its obligations, thereby increasing the company's access to trade credit. Thus, the clarity and lack of complexity in financial disclosures directly influence credit-based financing. These findings align with existing research in this domain, such as Li et al. (2024). In essence, transparent and understandable financial information acts as a credible signal to creditors, bolstering their confidence in the firm's financial health and repayment capacity. This, in turn, increases creditors' willingness to extend financial resources and ultimately facilitates more favorable trade credit conditions for firms. Therefore, improving the quality and transparency of financial disclosures is not merely a regulatory or ethical imperative but also an effective strategy for enhancing financing efficiency in competitive markets. The results from the second hypothesis indicate that product market competition significantly moderates the relationship between financial information readability and access to credit financing. The negative and significant interaction term reveals that heightened competition diminishes the positive effect of readability on credit access. In highly competitive industries, where numerous firms vie for market share, managers are compelled to offer greater benefits to stakeholders to capture a larger market segment. This intense rivalry for resources can create challenges in securing trade credit, leading to reduced access. These findings are partially consistent with prior work, such as Li et al. (2024). Specifically, under conditions of high market competition, firms face increased pressure on their financial resources as they strive to offer competitive terms to stakeholders. In such an environment, even high levels of financial transparency may be insufficient to ease credit constraints, as creditors perceive higher competitive risks and consequently adopt more cautious and stringent lending practices. This finding underscores the complexity of financing in competitive markets, indicating that access to credit is not solely a function of information quality but is also critically shaped by external market conditions. هدف پژوهش حاضر بررسی نقش خوانایی اطلاعات مالی بر دستیابی به تأمین مالی اعتباری با تأکید بر اثربخشی رقابت در بازار محصول است. نمونۀ آماری، کلیۀ شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران است که با استفاده از روش حذف سیستماتیک، درنهایت 141 شرکت انتخاب و در دورۀ زمانی 10ساله بین سال‌های 1393 الی 1402 بررسی شد. نتایج حاصل از آزمون فرضیۀ اول پژوهش نشان داد که میان خوانایی اطلاعات مالی و دستیابی به تأمین مالی اعتباری رابطۀ مستقیم و معناداری وجود دارد. نتیجۀ آزمون فرضیۀ دوم نشان داد که رقابت در بازار محصول بر رابطۀ میان خوانایی اطلاعات مالی و دستیابی به تأمین مالی اعتباری تأثیرگذار است. درواقع با تعامل رقابت در بازار محصول و خوانایی اطلاعات مالی، دستیابی به تأمین مالی اعتباری کاهش خواهد یافت؛ بنابراین، رقابت در بازار محصول می‌تواند دسترسی شرکت‌ها به تأمین مالی اعتباری را محدود کند.

https://amf.ui.ac.ir/article_29937_809db9dfb26076d32841e9ca2914d21b.pdf